Compression encoding means a series of signal processing technologies for transmitting digitized information through a communication line or storing the information in a form suitable for a storage medium. Media, such as a picture, an image and voice, may be the subject of compression encoding. In particular, a technology performing compression encoding on an image is called video image compression.
Next-generation video content will have features of high spatial resolution, a high frame rate, and high dimensionality of scene representation. Processing such content will result in a tremendous increase in terms of memory storage, a memory access rate, and processing power.
Therefore, there is a need to design a coding tool for processing next-generation video content more efficiently.
In particular, a graph is a data representation form useful for describing information about the relationship between pixels, and a graph-based signal processing method of performing processing by expressing information about the relationship between pixels in a graph form. A graph Fourier transform (GFT) is used when processing an image/video, including compression and noise removal. An advantage of the GFT is the ability to be adapted to the characteristics of signals with respect to discontinuous locations that are signaled and expressed by a graph. The use of the GFT when performing a block-based process is first introduced into depth map coding, and high frequency edge structures may be easily expressed in a low bit rate. Edges having a small pixel gradient are expressed as an additional optimized weight w∈(0, 1). A main object of the GFT is to require eigen decomposition that may be a complex number. Furthermore, transform operation requires O(N2) operations. In this case, some graphs are selected as template, and corresponding eigen vectors are previously calculated and stored. However, such a method can be applied to only small block sizes, that is, 4×4 and 8×8, and the number of different templates considered is relatively small.
In order to handle complexity related to the GFT, a graph-based lifting transform (GBLT) may be applied and may be applied to irregular graphs. Complexity can be much reduced than the GFT using localized filtering. Lifting regarding graphs have been applied to image noise removal and video compression, but they are global transforms and applied to the entire image. Lifting may be applied to the block-based coding of depth maps, surpasses performance of discrete cosine transform (DCT)-based coding, and has results comparable to the use of the GFT.
In this case, a simple lifting-based transform design well operates with respect to piecewise constant images, but does not provide better advantages than the DCT with respect to more common signals, such as natural images or intra prediction residuals.